Method of managing a refrigeration system

ABSTRACT

A method for monitoring the food product and refrigeration system performance of a remote location includes a management center in communication with a remote location through a communication network. The management center receives performance information of the refrigeration system at the remote location and employs software modules to analyze the performance information, diagnose system conditions, and provide alarms for food safety issues, refrigeration system component failure, and indicate maintenance conditions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/288,551 filed May 3, 2001.

FIELD OF THE INVENTION

The present invention relates to managing a refrigeration system and,more particularly, to monitoring and evaluating food inventory andequipment of a food retailer.

BACKGROUND OF THE INVENTION

Produced food travels from processing plants to retailers, where thefood product remains on display case shelves for extended periods oftime. In general, the display case shelves are part of a refrigerationsystem for storing the food product. In the interest of efficiency,retailers attempt to maximize the shelf-life of the stored food productwhile maintaining awareness of food product quality and safety issues.

For improved food quality and safety, the food product should not exceedcritical temperature limits while being displayed in the grocery storedisplay cases. For uncooked food products, the product temperatureshould not exceed forty-one (41) degrees Fahrenheit. Above this criticaltemperature limit, bacteria grow at a faster rate. In order to maximizethe shelf life and safety of the food product, retailers must carefullymonitor the food product stored therein. In general, monitoring of thetemperature of the food product enables determination of the bacterialgrowth rates of the food product. To achieve this, refrigeration systemsof retailers typically include temperature sensors within the individualrefrigeration units. These temperature sensors feed the temperatureinformation to a refrigeration system controller. Monitoring of the foodproduct involves information gathering and analysis.

The refrigeration system plays a key role in controlling the quality andsafety of the food product. Thus, any breakdown in the refrigerationsystem or variation in performance of the refrigeration system can causefood quality and safety issues. Thus, it is important for the retailerto monitor and maintain the equipment of the refrigeration system toensure its operation at expected levels.

Further, refrigeration systems generally require a significant amount ofenergy to operate. The energy requirements are thus a significant costto food product retailers, especially when compounding the energy usesacross multiple retail locations. As a result, it is in the bestinterest of food retailers to closely monitor the performance of therefrigeration systems to maximize their efficiency, thereby reducingoperational costs.

Monitoring food product quality and safety, as well as refrigerationsystem performance, maintenance and energy consumption are tedious andtime-consuming operations and are undesirable for retailers to performindependently. Generally speaking, retailers lack the expertise toaccurately analyze time and temperature data and relate that data tofood product quality and safety, as well as the expertise to monitor therefrigeration system for performance, maintenance and efficiency.Further, a typical food retailer includes a plurality of retaillocations spanning a large area. Monitoring each of the retail locationson an individual basis is inefficient and often results in redundancies.

Therefore, it is desirable in the industry to provide a method formonitoring the food product of a plurality of remote retailers. Themethod should allow a user to accurately determine the quality andsafety of the food product as a function of the temperature history andlength of time stored. Further, the method should provide an alarmingroutine for signaling when the food product has crossed particularquality and safety limits. The method should also monitor therefrigeration systems of the remote retailers for performance,maintenance and efficiency. The method should monitor multiple locationsfor performance comparison purposes, to avoid redundancies betweenremote locations and to provide the expertise required to accuratelyanalyze characteristics of individual remote locations.

SUMMARY OF THE INVENTION

Accordingly, the present invention provides a method for monitoring andmanaging a refrigeration system of a remote location. The methodincludes a communication network and a management center incommunication with the remote location through the communicationnetwork. The management center receives information from the remotelocation regarding performance of the refrigeration system, whereby themanagement center analyzes and evaluates the information for alteringoperation of the refrigeration system thereby improving the performance.

The method of the present invention further provides several alarmingroutines for alerting a user of specific scenarios occurring at theremote location. A first set of alarms are directed toward food qualityand safety concerns, alerting the management center and the remotelocation of potential issues with food quality and safety. A second setof alarms are directed toward components of the refrigeration system foralerting failure of particular components, as well as preventativemaintenance requirements of particular components.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from thedetailed description and the accompanying drawings, wherein:

FIG. 1 is a schematic overview of a system for remotely monitoring andevaluating a remote location, in accordance with the principles of thepresent invention;

FIG. 2 is a schematic view of an exemplary refrigeration systemaccording to the principles of the present invention;

FIG. 3 is a frontal view of a refrigeration case of the refrigerationsystem of FIG. 2;

FIG. 4 is a graph displaying cyclical temperature effects on bacteriagrowth within the refrigeration system;

FIG. 5 is a graphical representation of a time-temperature method formonitoring bacteria growth within the refrigeration system;

FIG. 6 is a graphical representation of a degree-minute method formonitoring bacteria growth within the refrigeration system;

FIG. 7 is a graphical representation of a bacteria count method formonitoring bacteria growth within the refrigeration system;

FIG. 8 is a flowchart outlining a method of calculating a food safetyindex according to the principles of the present invention;

FIG. 9 is a flowchart outlining a method of calculating a food qualityindex according to the principles of the present invention;

FIG. 10 is a schematic view of an energy usage algorithm in according tothe principles of the present invention;

FIG. 11 is a screen-shot of a temperature data sheet used in conjunctionwith the energy usage algorithm;

FIG. 12 is a schematic view of a temperature data routine;

FIG. 13 is a screen-shot of a temperature data import sheet;

FIG. 14 is a schematic view of an actual site data routine implementedin the energy usage algorithm;

FIG. 15 is a screen-shot of a store specification component of theactual site data routine;

FIG. 16 is a screen-shot of a new site data component of the actual sitedata routine;

FIG. 17 is a screen-shot of a core calculator implemented with theenergy usage algorithm;

FIG. 18 is a schematic view of a power monitoring routine;

FIG. 19 is a schematic view of an alarming routine;

FIG. 20 is a screen-shot of the power monitoring routine;

FIG. 21 is a schematic view of a design set-up routine;

FIG. 22 is a screen-shot of the design set-up routine;

FIG. 23 is a schematic view of a design results routine;

FIG. 24 is a screen-shot of the design results routine;

FIG. 25 is a screen-shot of a temperature variation routine;

FIG. 26 is a screen-shot showing charts summarizing results of theenergy usage algorithm;

FIG. 27A is a schematic of a dirty condenser algorithm;

FIG. 27B is a flowchart outlining the dirty condenser algorithm;

FIG. 28 is a schematic of a discharge temperature algorithm;

FIGS. 29A and 29B are respective schematics of suction superheat anddischarge superheat monitoring algorithms;

FIG. 30 is a schematic of service call algorithm;

FIG. 31 is a schematic diagram of energy saving algorithms implementedby the system of the present invention;

FIG. 32 is a graph of alarming conditions and actions in response toeach;

FIG. 33 is a schematic view of the alarming conditions implemented bythe system of the present invention; and

FIG. 34 is a screen-shot of a user interface of the system formonitoring a particular food storage case of a particular location.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments is merelyexemplary in nature and is in no way intended to limit the invention,its application, or uses.

With reference to FIGS. 1A and 1B, the present invention provides asystem 10 for remote monitoring, diagnosis and prognosis of foodinventory and equipment of a retailer. The system 10 includes amanagement center 12 in communication with a remote location 14, such asa food retail outlet, having food inventory and equipment, such as arefrigeration system, HVAC system, lighting and the like, therein. Acommunication network 16 is provided for operably interconnecting themanagement center 12 and the remote location 14 enabling informationtransfer therebetween. The communication network 16 preferably includesa dial-up network, TCP/IP, Internet or the like. It will be appreciatedby those skilled in the art, that the management center 12 may be incommunication with a plurality of remote locations 14 through thecommunication network 16. In this manner, the management center 12 isable to monitor and analyze operation of multiple remote locations 14.

The management center 12 gathers operational data from the remotelocation 14 to analyze performance of several aspects of the remotelocation 14 through post-processing routines. Initially, the managementcenter 12 may process temperature information for calculating foodsafety and food quality indices (FSI and FQI, respectively), asdescribed in further detail below. Calculated values for FSI and FQI maybe used by the management center 12 to alert a remote location 14 offood safety and quality performance. In this manner, the remote location14 is able to adjust the operation of its systems to improveperformance.

Also, the management center 12 may gather and process energy consumptioninformation for its energy using equipment including various componentsof the refrigeration system and the refrigeration system as a whole. Ananalysis of the energy consumption of the energy using equipment enablesthe management center 12 to evaluate the overall efficiency thereof andidentify any problem areas therewith. Finally, the management center 12may gather information specific to each component of the refrigerationsystem for evaluating the maintenance measures each component mayrequire. Both routine and preventative maintenance may be monitored andevaluated, thereby enabling the management center 12 to alert the remotelocation of potential equipment malfunctions. In this manner, overallefficiency of the refrigeration system may be enhanced.

Additionally, the management center 12 provides a data warehouse 18 forstoring historical operational data for the remote location 14. The datawarehouse 18 is preferably accessible through the communication network16 utilizing commercially available database software such as MicrosoftAccess™, Microsoft SQL-Server™, ORACLE™, or any other database software.

The system 10 is remotely accessible through a graphical user interface20 via a third-party computer system through the communication network.In an exemplary embodiment, a remote user may log into the system 10through the Internet to view operational data for the remote location14. The third-party computer system may include any web-enabled GUIknown in the art, including but not limited to a computer, a cellularphone, a hand-held portable computer (e.g., Palm Pilot™) or the like.

The GUI 20 provides a view into the system 10 and allows the user to seethe data for the remote location 14 via a standard web browser. The GUI20 also provides access to software modules 22, which preferably run onone or more servers 24. The GUI 20 can provide this access using only astandard web browser and an Internet connection. Maintenance managerswill use the GUI 20 to receive alarms for a specific remote location 14,acknowledge alarms, manually dispatch work orders based on the alarms,make changes to set points, ensure that a remote location 14 isperforming as required (by monitoring case temperatures, rack pressures,etc.), and check a remote location 14 after the receipt of an alarm.

More specifically, the system 10 will make use of existing networkinfrastructure to add value to users who use the system for collectingand/or aggregating data. This value includes speeding up (andautomating) the data collection process and enabling the aggregation ofdata to be performed automatically. The information that is retrievedfrom a remote location 14 resides on servers 24. Further, the systemallows the ability to add software modules 22 to the server 24 that willextract additional information from the data. Examples are analyzingtrend information of pressure and compressor status over a period oftime and extracting performance degradation characteristics of thecompressors.

FIG. 1B shows a diagram of the communications network 16. Multipleremote locations 14 exist behind a corporate firewall 26 and that thedata behind the firewall 26 must be pushed to a server 24, which existsoutside the firewall 26. Users are able to access the information via anInternet connection in the standard browser. In general, the user shouldbe given the impression that he/she is always going through the server24 to retrieve information from the remote location 14. It is possiblefor a user to view both real-time data generated at the site andaggregated data in a single view. Using this architecture, softwaremodules 22 can be easily added to perform functions on the data.

Web-based navigation is accomplished by the GUI 20, which will beinterfaced for all of the software modules 22. Alarm monitoring, energyanalysis, food quality, and maintenance software modules 22 aredescribed below, and each are accessible via the GUI 20. A softwaremodule 22 may even be provided for enabling the user to completelyconfigure a controller, as discussed in further detail below. Itsprimary use will be during initial configuration of the controller. Awork order module provides the capability to enter and track work ordersfor managing the maintenance schedule of the equipment of the remotelocation 14. An asset management module provides the capability to enterand track assets and view asset history.

The GUI 20 also offers a number of standard screens for viewing typicalsite data. A store summary screen is provided and lists the status ofthe refrigeration, building control systems and the like. A producttemperature summary screen displays product temperatures throughout thestore when using product temperature probes. An alarm screen enables theuser to see the status of all alarms. The alarm screen providesinformation about particular alarms and enables the alarm to beacknowledged and reset, as discussed in further detail hereinbelow.Basic alarm viewing/notification capability is provided and includes theability to view an alarm, acknowledge an alarm, and receive notificationof the alarm. Notification is either via GUI/browser, e-mail, facsimile,page, or text message (SMS/e-mail) to a cellular telephone. Each alarmtype has the capability of selecting whether notification is requiredand what (and to whom) the notification method will be.

The GUI 20 provides the capability to display historical (logged) datain a graphical format. In general, the graph should be accessible fromthe screen with a single click. Data is overlaid from different areas(e.g. case temperature with saturated suction temperature) on a singlegraph. Some historical data may be stored on a server 24. In general,the display of this data should be seamless and the user should not knowthe source of the data.

The GUI 20 provides the capability to display aggregated remote locationdata, which should be displayed as aggregated values and includes thecapability to display power and alarm values. These views may beselected based on user requirements. For example, the GUI 20 providesthe capability to display aggregated remote location power data for anenergy manager log in and aggregated alarm data for a maintenancemanager log in. The GUI 20 will provide a summary-type remote locationscreen with power and alarms for the remote location 14 as a default.

The GUI 20 provides the capability to change frequently used set pointsdirectly on the appropriate screen. Access to other set points isachieved via a set point screen that can be easily navigated with oneclick from the GUI 20. In general, applications on controllers have manyset points, the majority of which are not used after the initial setup.

Returning to FIG. 1A, the remote location 14 may further include apost-processing system 30 in communication with the components of therefrigeration system through the controller. The post-processing system30 is preferably in communication with the controller through a dial-up,TCP/IP, or local area network (LAN) connection. The post-processingsystem 30 provides intermediate processing of gathered data, which isanalyzed to provide lower-level, local warnings. These lower-level,local warnings are in contrast to more detailed, higher-level warningsprovided by the post-processing routines of the management center 12.The post-processing system 30 is preferably an “In-Store InformationServer,” or ISIS, that also provides a web gateway functionality. TheISIS platform of the preferred embodiment is a JACE/controller/webserver commercially available from Tridium, Inc., of Richmond, Va.,U.S.A.

With reference to FIGS. 2 and 3, an exemplary refrigeration system 100of the remote location 14 preferably includes a plurality ofrefrigerated food storage cases 102. The refrigeration system 100includes a plurality of compressors 104 piped together with a commonsuction manifold 106 and a discharge header 108 all positioned within acompressor rack 110. A discharge output 112 of each compressor 102includes a respective temperature sensor 114. An input 116 to thesuction manifold 106 includes both a pressure sensor 118 and atemperature sensor 120. Further, a discharge outlet 122 of the dischargeheader 108 includes an associated pressure sensor 124. As described infurther detail hereinbelow, the various sensors are implemented forevaluating maintenance requirements.

The compressor rack 110 compresses refrigerant vapor that is deliveredto a condenser 126 where the refrigerant vapor is liquefied at highpressure. The condenser 126 includes an associated ambient temperaturesensor 128 and an outlet pressure sensor 130. This high-pressure liquidrefrigerant is delivered to a plurality of refrigeration cases 102 byway of piping 132. Each refrigeration case 102 is arranged in separatecircuits consisting of a plurality of refrigeration cases 102 thatoperate within a certain temperature range. FIG. 2 illustrates four (4)circuits labeled circuit A, circuit B, circuit C and circuit D. Eachcircuit is shown consisting of four (4) refrigeration cases 102.However, those skilled in the art will recognize that any number ofcircuits, as well as any number of refrigeration cases 102 may beemployed within a circuit. As indicated, each circuit will generallyoperate within a certain temperature range. For example, circuit A maybe for frozen food, circuit B may be for dairy, circuit C may be formeat, etc.

Because the temperature requirement is different for each circuit, eachcircuit includes a pressure regulator 134 that acts to control theevaporator pressure and, hence, the temperature of the refrigeratedspace in the refrigeration cases 102. The pressure regulators 134 can beelectronically or mechanically controlled. Each refrigeration case 102also includes its own evaporator 136 and its own expansion valve 138that may be either a mechanical or an electronic valve for controllingthe superheat of the refrigerant. In this regard, refrigerant isdelivered by piping to the evaporator 136 in each refrigeration case102. The refrigerant passes through the expansion valve 138 where apressure drop causes the high pressure liquid refrigerant to achieve alower pressure combination of liquid and vapor. As hot air from therefrigeration case 102 moves across the evaporator 136, the low pressureliquid turns into gas. This low pressure gas is delivered to thepressure regulator 134 associated with that particular circuit. At thepressure regulator 134, the pressure is dropped as the gas returns tothe compressor rack 110. At the compressor rack 110, the low pressuregas is again compressed to a high pressure gas, which is delivered tothe condenser 126, which creates a high pressure liquid to supply to theexpansion valve 138 and start the refrigeration cycle again.

A main refrigeration controller 140 is used and configured or programmedto control the operation of the refrigeration system 100. Therefrigeration controller 140 is preferably an Einstein Area Controlleroffered by CPC, Inc. of Atlanta, Ga., U.S.A., or any other type ofprogrammable controller that may be programmed, as discussed herein. Therefrigeration controller 140 controls the bank of compressors 104 in thecompressor rack 110, via an input/output module 142. The input/outputmodule 142 has relay switches to turn the compressors 104 on an off toprovide the desired suction pressure. A separate case controller (notshown), such as a CC-100 case controller, also offered by CPC, Inc. ofAtlanta, Ga., U.S.A., may be used to control the superheat of therefrigerant to each refrigeration case 102, via an electronic expansionvalve in each refrigeration case 102 by way of a communication networkor bus. Alternatively, a mechanical expansion valve may be used in placeof the separate case controller. Should separate case controllers beutilized, the main refrigeration controller 140 may be used to configureeach separate case controller, also via the communication bus. Thecommunication bus may either be a RS-485 communication bus or a LonWorksEchelon bus that enables the main refrigeration controller 140 and theseparate case controllers to receive information from each refrigerationcase 102.

Each refrigeration case 102 may have a temperature sensor 146 associatedtherewith, as shown for circuit B. The temperature sensor 146 can beelectronically or wirelessly connected to the controller 140 or theexpansion valve for the refrigeration case 102. Each refrigeration case102 in the circuit B may have a separate temperature sensor 146 to takeaverage/min/max temperatures or a single temperature sensor 146 in onerefrigeration case 102 within circuit B may be used to control eachrefrigeration case 102 in circuit B because all of the refrigerationcases 102 in a given circuit operate at substantially the sametemperature range. These temperature inputs are preferably provided tothe analog input board 142, which returns the information to the mainrefrigeration controller 140 via the communication bus.

Additionally, further sensors are provided and correspond with eachcomponent of the refrigeration system and are in communication with therefrigeration controller. Energy sensors 150 are associated with thecompressors 104 and condenser 126 of the refrigeration system 100. Theenergy sensors 150 monitor energy consumption of their respectivecomponents and relay that information to the controller 140.

Circuits and refrigeration cases 102 of the refrigeration system 100include a screen 152 illustrating the type and status of therefrigeration case 102 or circuit. Temperatures are displayed viagraphical means (e.g. a thermometer) with an indication of set point andalarm values. The screen 152 supports a display of case temperatures(i.e. return, discharge, defrost termination, coil in, coil out, andproduct temperatures) and the status of any digital inputs (i.e.cleaning, termination, etc.). The screen 152 also displays a defrostschedule and the type of termination (i.e. time, digital, temperature)for the last defrost. In general, all information related to arefrigeration case 102 or circuit will be displayed on or accessiblethrough the screen 152.

A screen 154 is also provided to graphically display the status of eachconfigured suction group. Discharge and suction pressures are displayedas gauges intended to be similar to the gauge set a refrigerationmechanic would use. The corresponding saturated suction temperature willbe displayed as well. In general, suction groups are displayedgraphically with icons that represent each compressor 104. The status ofthe compressors 104 is shown graphically, as well as the status of anyconfigured unloaders. In general, all status information for a suctiongroup is displayed on the screen 154.

A screen 156 is also provided to graphically display the status of eachconfigured condenser 126. The suction and discharge pressure of thecondenser 126 are displayed as gauges intended to be similar to a gaugeset a refrigeration mechanic would use. The corresponding condensingtemperature will be displayed as well. In general, the condenser 126should be displayed graphically with icons that represent each fan ofthe condenser 126. A status of the fans is shown graphically. Ingeneral, all status information for a condenser 126 will be displayed onthe screen 156.

A screen (not shown) will also be provided for roof top units (notshown), the detailed description of which is foregone. The status of theroof top unit will be shown with animated graphics (fan, airflow,cooling, heating, as animated pieces). The screen will also show thespace temperature, supply temperature, etc. The set point and alarmvalues are shown for the space temperature. Humidity and humiditycontrol may also be shown if configured.

It will be appreciated that the hereindescribed refrigeration system ismerely exemplary in nature. The refrigeration system of the remotelocation may vary as particular design requirements of the locationdictate.

Remote locations 14 having refrigeration systems 100 typically includefood-product retailers and the like. The food-product retailers areconcerned with both the safety and the aesthetic quality of the foodproducts they sell. Generally, bacteria that pose a threat to humanhealth are referred to as “pathogen” bacteria and grow quickly when thetemperature of their host product rises above a certain thresholdtemperature. For example, forty-one (41) degrees Fahrenheit isrecognized industry-wide as the temperature below which most pathogensgrow slowly and below which perishable food products should be stored.Bacteria that diminish the quality (color, smell, etc.) of a foodproduct are referred to as “spoiler” bacteria and have growth rates thatvary from product to product. Spoiler bacteria generally grow morequickly than pathogen bacteria. Thus, a food product's quality mayappear to be of poor color or smell but still safe for humanconsumption. Bacteria populations and disease risk are a function ofboth the frequency and severity of over-temperature product conditions.Biological growth rates increase non-linearly, as a product warms pastabout forty-one (41) degrees Fahrenheit. For example, a product at aboutfifty-one (51) degrees Fahrenheit is more likely to host large coloniesof toxic bacteria than a product at about forty-four (44) degreesFahrenheit. However, there may be as much risk from having the productin a case at about forty-four (44) degrees Fahrenheit for a longerperiod of time than in a single case at about fifty-one (51) degreesFahrenheit for a shorter period of time.

The temperature of a host food product, as mentioned above,significantly influences the rate at which bacteria, whether spoiler orpathogen, grows. Generally, conventional refrigeration systems functionusing a cyclical temperature strategy. According to the cyclicaltemperature strategy, low and high temperature set points arepredetermined. The refrigeration system operates to cool the productsuntil the low temperature set point is achieved. Once achieving thelow-temperature set point, the refrigeration system ceases cooling thefood product and the temperature is allowed to rise until meeting thehigh-temperature set point. Once the high-temperature set point isachieved, cooling resumes until meeting the low-temperature set point.

With particular reference to FIG. 4, cyclical temperature control andits effects on bacterial growth will be discussed in detail. An increasein temperature increases the rate at which bacteria grows. Time period Aof the chart of FIG. 4 shows an exemplary increase in temperature fromapproximately thirty (30) degrees Fahrenheit to approximately fifty (50)degrees Fahrenheit. An increase in bacteria count is associated with therise in temperature. The bacteria count of time period A rises fromapproximately ten thousand (10,000) counts/gm to approximately fortythousand (40,000) counts/gm. Time period B shows an exemplary decreasein temperature from the fifty (50) degrees Fahrenheit achieved at theend of time period A, to approximately thirty (30) degrees Fahrenheit. Adecrease in the rate at which the bacteria grows is associated with thedecrease in temperature. It is important to note, however, that thebacteria count still increases and only slows significantly when thetemperature cools to about thirty (30) degrees Fahrenheit. The exemplaryincrease in bacteria count rises from approximately forty thousand(40,000) counts/gm to approximately seventy thousand (70,000) counts/gm.The first half of time period B reflects a significant rate of growth ofbacteria while a decrease in the rate is not achieved until the latterhalf of time period B. Thus, re-chilling or re-freezing of food productsdoes not kill or reduce the bacteria-count, but simply reduces thegrowth rate of the bacteria.

The system of the present invention implements a variety of monitoringand alarming routines provided in the form of software. Components ofthese routines include product temperature monitoring and alarming. Toachieve this, the routines include a time/temperature alarming routine,a degree/minutes alarming routine and a bacteria-count alarming routine.While each of these routines is described in detail hereinbelow, itshould be noted that in terms of food safety and quality they are listedin order of increasing effectiveness. In other words, thetime/temperature alarming routine provides a good means of monitoringproduct temperature while the bacteria-count alarming routine providesthe most effective means.

With reference to FIG. 5, the time/temperature alarming routine will bedescribed in detail. Initially, both time and temperature set points areprovided. In the exemplary embodiment of FIG. 5, the time set point issixty (60) minutes and the temperature set point is about forty (40)degrees Fahrenheit. The time and temperature set points are combined toprovide an alarming point. In the exemplary case, the alarming pointwould be the point at which the product has been at a temperaturegreater than forty (40) degrees Fahrenheit for longer than sixty (60)minutes. With reference to alarm scenario R1 of FIG. 5, the producttemperature passes forty (40) degrees Fahrenheit at point P1. Thus, thesixty (60) minute clock begins running at point P1. If the producttemperature has not fallen back below about forty (40) degreesFahrenheit within the sixty (60) minute timeframe then an alarm issignaled. Point M1 represents the point at which sixty (60) minutes havepassed and the temperature has remained over about forty (40) degreesFahrenheit. Therefore, in accordance with the time/temperature routine,an alarm would be signaled at point M1.

Although the above-described time/temperature routine is a good methodof monitoring product temperature, it retains specific disadvantages.One disadvantage is that bacteria count is not considered. This is bestillustrated with reference to alarm scenario R2. As can be seen, theproduct temperature of alarm scenario R2 increases, approaching theabout forty (40) degrees Fahrenheit temperature set point without evercrossing it. As discussed above, with respect to FIG. 4, increases intemperature, even though below the about forty (40) degrees Fahrenheittemperature set point, results in increased rate of bacteria growth.Thus, although the time/temperature routine would not signal an alarm inalarm scenario R2, bacteria growth would continue, approaching undesiredlevels of bacteria count over time.

With reference to FIG. 6, the degree/minutes alarming routine will bedescribed in detail. Initially, a degree/minutes set point isdetermined. In the exemplary case, the degree/minutes set point is abouteight hundred (800). This value is provided as an average valuedetermined from historical data and scientific testing and analysis ofbacteria growth. In this manner, bacteria growth is considered whendetermining whether an alarm is signaled. With reference to alarmscenarios R1 and R2 of FIG. 6, the degree/minute alarming routineintegrates the ideal product temperature curve (i.e., area above “idealtemp” line) with respect to time. If the integration results in a valueof about eight hundred (800) or greater, an alarm is signaled. In theexemplary case both alarm scenarios R1, R2 would result in an alarm.Alarm scenario R1 would most likely signal an alarm prior to alarmscenario R2. This is because the bacteria growth rate would besignificantly higher for alarm scenario R1. An alarm would be signaledin alarm scenario R2 because, although the product temperature of alarmscenario R2 never rises above an accepted temperature (i.e., about forty(40) degrees Fahrenheit), the borderline temperature of alarm scenarioR2 results in a high enough bacteria growth rate that undesired bacterialevels would be achieved in time.

With reference to FIG. 7, the bacteria-count alarming routine will bedescribed in detail. Initially, an alarm set point is determinedaccording to the maximum acceptable bacteria count for the product. Inthe exemplary case, the alarm set point is approximately one hundredtwenty thousand (120,000) counts/gm. FIG. 7, similarly to FIG. 4, showsa cyclical-temperature curve and a bacteria-count curve. Thebacteria-count routine periodically calculates the bacteria count for agiven temperature at a given time, thereby producing the bacteria-countcurve. Given the cyclical temperature of the exemplary case of FIG. 7,neither of the aforementioned alarming routines would signal an alarm.However, once the bacteria count is greater than the approximately onehundred twenty thousand (120,000) counts/gm alarm set point, an alarm issignaled. As noted previously, the bacteria count alarming routine isthe most effective of those described herein. The effectiveness of thebacteria count alarming routine is a result of the direct relation to anactual bacteria count of the product.

Bacteria count is calculated for each type of bacteria (i.e., pathogen,spoiler), and is a function of a base bacteria count, time, producttype, and temperature. Initially, base bacteria counts (N_(o)) areprovided for each type of bacteria. As provided by the presentinvention, an exemplary base bacteria count for pathogen bacteria isabout one hundred (100) counts/gram and for spoiler bacteria is aboutten thousand (10,000) counts/gram. These values have been determinedthrough experiment and analysis of the bacteria types. Both the producttype and temperature determines the rate at which a particular type ofbacteria will grow. The present invention further provides initialtemperatures for both pathogen and spoiler bacteria, at which, theirrespective growth is effectively stopped. In an exemplary embodiment,the initial temperature for pathogens is twenty-nine (29) degreesFahrenheit and for spoilers is eighteen and one-half (18.5) degreesFahrenheit. Similarly to the initial bacteria count values, these valueshave been determined through experiment and analysis of the bacteriatypes. In general, experimental bacteria counts for both pathogens andspoilers were plotted with respect to temperature. A line wasinterpolated for each and extrapolated to find their respectivey-intercepts, or temperature values for zero growth.

Algorithms are provided in the form of software modules that can resideeither in software module twenty-two (22) or post-processing systemthirty (30) (ISIS). Both spoiler and pathogen bacteria are calculatedbased on time and temperature measured by an infrared temperature guntwo hundred (200) or a food product simulator two hundred two (202). Afood quality alarm is generated when the spoiler bacteria multiplies ten(10) times and food safety alarm is generated when pathogen bacteriamultiplies five (5) times. Additionally, index calculation, namely FQIand FSI, is done to rate the performance of a fixture, department orstore within a chain. As a result the FSI determination uses worst-casevalues to provide a conservative valuation of food safety risk and tominimize the possibility of an undetected food safety problem. The FQIenables monitoring of the aesthetic quality of products, therebyenabling the remote location to increase the shelf life of perishableproducts resulting in increased customer satisfaction and cost savings.

With reference to FIG. 8, the algorithm for calculating the FSI will bedescribed in detail. The FSI of the present invention corresponds tobacterial risk levels and provides a method for relative-riskevaluation. Initially, at step 800, the temperature of a product samplefrom each of the product groups (P₁, P₂, . . . , P_(j)) will be measuredin each of the cases (C₁, C₂, . . . , C_(i)) (see FIG. 3). Thus, atemperature matrix is formed accounting for a sample of each of theproducts in each of the cases:

C₁: T₁₁ T₁₂ . . . T_(1j) C₂: T₂₁ T₂₂ . . . T_(2j) C_(i): T_(i1) T_(i2) .. . T_(ij)

After the product temperatures are measured, the maximum producttemperature is determined for each case (C₁, C₂, . . . , C_(i)), at step810, as follows:

MAX(T ₁₁ , T ₁₂ , . . . , T _(1j))=T _(1MAX)

MAX(T ₂₁ , T ₂₂ , . . . , T _(2j))=T _(2MAX)

MAX(T _(i1) , T _(i2) , . . . , T _(ij))=T _(iMAX)

Each food product (P₁, P₂, . . . , P_(j)) has an associated expectedshelf life rating (S₁, S₂, . . . , S_(j)). The shelf life ratings (S₁,S₂, . . . , S_(j)), designated at step 820, are based on scientificallydeveloped and experimentally confirmed micro-organism growth equations.At step 830, the maximum shelf life rating (S_(1MAX), S_(2MAX), . . . ,S_(jMAX)) for the products (P₁, P₂, . . . , P_(j)) within each case (C₁,C₂, . . . , C_(j)) is determined as follows:

MAX(S ₁₁ , S ₁₂ , . . . , S _(1j))=S _(1MAX)

MAX(S ₂₁ , S ₂₂ , . . . , S _(2j))=S _(2MAX)

MAX(S _(i1) , S _(i2) , . . . , S _(ij))=S _(iMAX)

Each food product (P₁, P₂, . . . , P_(j)) further has an associated basebacteria count (N_(o1), N_(o2), . . . , N_(oj)). At step 840, themaximum base bacteria count (N_(o1), N_(o2), . . . , N_(oj)) for theproducts (P₁, P₂, . . . , P_(j)) within each case (C₁, C₂, . . . ,C_(i)) is determined as follows:

MAX(N _(o11) , N _(o12) , . . . , N _(o1j))=N _(o1MAX)

MAX(N _(o21) , N _(o22) , . . . , N _(o2j))=N _(o2MAX)

MAX(N _(oi1) , N _(oi2), . . . , N_(oij))=N _(oiMAX)

Having determined the maximum temperature, the maximum shelf-life ratingand the maximum base bacteria count for the products (P₁, P₂, . . . ,P_(j)) in each case (C₁, C₂, . . . , C_(i)), a bacteria count (N_(1t),N_(2t), . . . , N_(it)) is calculated for a specific time (t) for eachcase (C₁, C₂, . . . , C_(i)) at step 850. The bacteria count (N_(1t),N_(2t), . . . , N_(it)) is a function of the maximum producttemperature, the maximum base bacteria count, and the maximum shelf-liferating, as determined above, with respect to the type of bacteriaconcerned. The bacteria count is provided as:

N _(it) =N _(oimax)×2^(gi)

where

g _(i)=shelf life×[m×T _(p) +c] ²

In the case of food safety, the concerned bacteria are pathogens. Thus,the values m and c are the slope and intercept for the model generatedfor pathogen bacteria, discussed above.

Having determined the bacteria counts (N_(1t), N_(2t), . . . , N_(it))and the threshold maximum base bacteria counts (N_(o1MAX), N_(o2MAX), .. . , N_(ojMAX)), the food safety index (FSI) for each case (C₁, C₂, . .. , C_(i)) is calculated at step 870. The calculation of the FSI foreach case is determined by the following equation:

FSI _(i)=100×[1−[ In(N _(it) /N _(oiMAX))/In 2]×0.2]

As a result, FSI values for each case are calculated.

Bacteria populations and disease risk are a function of both thefrequency and severity of over-temperature product conditions.Biological growth rates increase non-linearly, as a product warms pastforty-one (41) degrees Fahrenheit. For example, a product at fifty-one(51) degrees Fahrenheit is more likely to host large colonies of toxicbacteria than a product at forty-four (44) degrees Fahrenheit. However,there may be as much risk from having the product in a case atforty-four (44) degrees Fahrenheit for a longer period of time than in asingle case at fifty-one (51) degrees Fahrenheit for a shorter period oftime. To account for this variation, an average safety factor FSI_(AVG)is used.

Having determined a FSI for each case of the refrigeration system,secondary parameters B and R are subsequently calculated at step 875.The secondary parameter B is equal to the number of cases and R is equalto the sum of all of the FSIs for the cases that has potentiallyhazardous food (PHF). At step 880, secondary parameters B and R are usedto calculate the average FSI, as follows:

FSI _(AVG) =R/B

Thus, the FSI for a department or store is provided as FSI_(AVG).

With particular reference to FIG. 9, the algorithm for calculating theFQI will be described in detail. Initially, at step 900, the temperatureof each of the product groups (P₁, P₂, . . . , P_(j)) will be measuredin each of the cases (C₁, C₂, . . . , C_(i)) (see FIG. 2). Thus, atemperature matrix is formed accounting for all of the products in allof the cases:

C₁: T₁₁ T₁₂ . . . T_(1j) C₂: T₂₁ T₂₂ . . . T_(2j) C_(i): T_(i1) T_(i2) .. . T_(ij)

After the product temperatures are measured, the average temperature foreach product group P within each case C is determined at step 910.

T _(1AVG)=AVG(T ₁₁ , T ₁₂ , . . . , T _(1j))

T _(2AVG)=AVG(T ₂₁ , T ₂₂ , . . . , T _(2j))

T _(iAVG)=AVG(T _(i1) , T _(i2) , . . . , T _(ij))

As discussed above with respect to the FSI, each food product has anassociated shelf-life rating (S₁, S₂, . . . , S_(j)). At step 920 of theFQI calculation, the average shelf-life rating (S_(1AVG), S_(2AVG), . .. , S_(jAVG)) for the products (P₁, P₂, . . . , P_(j)) within each case(C₁, C₂, . . . , C_(i)) is determined as follows:

AVG(S ₁₁ , S ₁₂ , . . . , S _(1j))=S _(1AVG)

AVG(S ₂₁ , S ₂₂ , . . . , S _(2j))=S _(2AVG)

AVG(S _(i1) , S _(i2) , . . . , S _(ij))=S _(iAVG)

As further discussed above, each food product (P₁, P₂, . . . , P_(j))has an associated base bacteria count (N_(o1), N_(o2), . . . , N_(oj)).At step 930, the average base bacteria count (N_(o1AVG), N_(o2AVG), . .. , N_(ojAVG)) for the products (P₁, P₂, . . . , P_(j)) within each case(C₁, C₂, . . . , C_(i)) is determined as follows:

AVG(N _(o11) , N _(o12) , . . . , N _(o1j))=N _(o1AVG)

AVG(N _(o21) , N _(o22) , . . . , N _(o2j))=N _(o2AVG)

AVG(N _(oi1) , N _(oi2) , . . . , N _(oij))=N _(oiAVG)

Furthermore, an ideal storage temperature TI is associated with eachproduct P. The product mixes for each case C are determined at step 940and are generally given as follows:

 C_(i)[P₁%, P₂%, . . . , P_(j)%]

Using the product mix values, a weighted average is determined for theideal temperature TI, at step 950, as follows:

Ideal Temperature TI:

TI _(1AVG) =TI ₁ P ₁%+TI ₂ P ₂%+ . . . +TI _(j) P _(j)%

TI _(2AVG) =TI ₁ P ₁%+TI ₂ P ₂%+ . . . +TI _(j) P _(j)%

TI _(iAVG) =TI ₁ P ₁%+TI ₂ P ₂%+ . . . +TI _(j) P _(j)%

Having determined the average temperature, the average shelf-life ratingand the average base bacteria count for the products (P₁, P₂, . . . ,P_(j)) in each case (C₁, C₂, . . . , C_(i)), a bacteria count (N_(1t),N_(2t), . . . , N_(it)) is calculated for a specific time (t) for eachcase (C₁, C₂, . . . , C_(i)). The bacteria count (N_(1t), N_(2t), . . ., N_(it)) is a function of the average product temperature, the averagebase bacteria count, and the average shelf-life rating, as determinedabove, with respect to the type of bacteria concerned. In the case offood quality, the concerned bacteria are spoiler. The bacteria count iscalculated as previously discussed hereinabove.

Having determined the bacteria counts (N_(1t), N_(2t), . . . , N_(it))and the average base bacteria counts (N_(o1AVG), N_(o2AVG), . . . ,N_(oiAVG)), the food quality index (FQI) for each case (C₁, C₂, . . . ,C_(i)) is calculated at step 970. The calculation of the FQI for eachcase is determined by the following equation:

FQI _(i)=100×[1−[ In(N _(it) /N _(oiAVG))/In 2]×0.1]

As a result, FQIs are calculated for each case C.

Having determined the FQI for each case C of the refrigeration system,secondary parameters B and R are subsequently calculated at step 975. Asbefore, secondary parameter B is equal to the number of cases and R isequal to the sum of all of the quality factors. At step 980, secondaryparameters B and R are used to calculate the average quality factorFQI_(AVG), as follows:

FQI _(AVG) =R/B

Thus, the FQI for a department or store is provided as FQI_(AVG).

The system further provides a method for estimating the shelf life ofproducts within a specific case as a function of historical temperaturedata and any occurrences (e.g. power outages and the like) at aparticular location. The shelf life estimation method is case based. Anew counter is started for each day and has a maximum length of five (5)days. Generally, food product turnover is less than five (5) days,however, the maximum length of days may vary. For each day, bacteriacount is determined, as described above, using the particulartemperatures experienced by the case for that day. In this manner, thegrowth of bacteria for the given case can be monitored and evaluated todetermine how much longer products put into the case on a particular daymay safely remain in the case. For example, the shelf life of a productthat has been put into a case one day ago is a function of thetemperatures experienced over the first day. At the same time, however,the shelf life of a product that has been in the case for three dayswill be determined as a function of the temperatures experienced overthose three days.

In a first preferred embodiment, the temperature measurements for eitherthe FSI or FQI calculation are achieved using a hand-held infraredtemperature sensor measurement device such as an infrared temperaturegun 200 (see FIG. 3) commonly known in the art during an “audit”process. It is anticipated that the gun 200 will measure thetemperatures of a sample of each product group and determine theaverage, minimum and maximum temperature values. In this manner, onlyone audit process is required to calculate both FSI and FQI. The auditprocess preferably occurs regularly (i.e., yearly, monthly, weekly,daily, etc.).

It is also anticipated that continuous food product temperaturemeasurement is achieved real-time, as opposed to an audit process. Forexample, a food product simulator 202 (see FIG. 3) may be disposed ineach refrigerator case (C_(i)) for each food product group (P_(j))within the refrigerator case (C_(i)). A detailed description of the foodproduct simulator is provided in co-pending application Ser. No.09/564,173, filed on May 3, 2000, with the United States Patent andTrademark Office, entitled “Wireless Method And Apparatus For MonitoringAnd Controlling Food Temperature,” hereby incorporated by reference. Theproduct group temperature samples are read by the controller 140 and arecontinuously monitored during a “monitor” process. It is anticipatedthat at least one simulator 202 will be present for each product group(P_(j)) in a particular case (C_(i)). The monitor process may recordtemperature values at a predetermined rate (i.e., every minute, 10minutes, etc.) that is operator programmable into the controller 140, orreal-time. The implementation of a food product simulator 202 isexemplary in nature and it is anticipated that other products andmethods can be used to achieve real-time or periodic sampling within thescope of the invention.

As discussed previously, the present invention provides a method forgathering and processing energy consumption information for variousequipment within a food retailer. Of particular importance is the energyconsumption of the refrigeration system 100. To monitor the energyconsumption performance of the refrigeration system 100, a softwaremodule 22 is provided that runs the hereindescribed algorithms androutines required. In the present embodiment, the software is providedas a Microsoft™ Excel™ workbook implementing the Visual Basicprogramming language. It is anticipated, however, that the software maybe provided in any one of a number of formats or programmed using anyone of a number of programming languages commonly known in the art.

With reference to FIG. 10, a schematic overview of the present methodand supporting software is shown. In general, the method of the presentinvention operates around a core calculator 210 that receivesinformation from an input block 212 and provides outputs to both anefficiency block 214 and a design block 216. The input block 212includes three main components. The first component is weather data 218provided as a look-up table, based on information from the AmericanSociety of Heating, Refrigerating and Air Conditioning Engineers, Inc.(ASHRAE) of Atlanta, Ga., U.S.A. The ASHRAE look-up table includesgeneral climate information for several cities throughout the UnitedStates and Canada, as averages over a ten-year period. With reference toFIG. 11, a screen-shot is provided displaying the ASHRAE data as itwould appear in an Excel™ workbook and FIG. 12 provides a schematiclayout of the ASHRAE component. The ASHRAE data includes both wet anddry bulb temperature data for the remote location 14 during particularmonths. As seen in FIG. 11, temperature information is provided forspecific cities based upon month and a bin temperature. The bintemperatures range from a maximum of one hundred twenty-six and one-half(126.5) degrees Fahrenheit and step down by increments of seven (7)degrees Fahrenheit. Reading FIG. 11, the number of hours a particularcity experiences a particular temperature in the particular month, isprovided. For example, during the month of January, Edmonton, Alberta,Canada experiences a dry bulb temperature of thirty-five (35) degreesFahrenheit for a total of eight (8) hours that month. Current ASHRAEdata may be imported, as shown in FIG. 13, thereby ensuring the mostcurrent data for the dependent calculations. The ASHRAE componentprovides output information for use by the core calculator.

The second component includes actual site data 220, which comprises bothstore specification and new site data components 222,224, respectively,as shown schematically in FIG. 14. The store specification component 222accounts for the various refrigeration components operating at aspecific remote location 14. With reference to FIG. 15, a screen-shot isprovided displaying an exemplary remote location 14 and its relatedrefrigeration components, as it would appear in an Excel™ workbook. Astandard component list is provided and only the information forequipment actually on-site is listed in the corresponding cells. Thisinformation includes: system name, size line-up and load (BTU/hr). Theinformation is provided per a rack type (i.e., low temperature rack,medium temperature rack, etc.). Particular information from the storespecification component 222 is also provided to the design block 216, asdescribed in further detail hereinbelow.

With reference to FIG. 16, a screen-shot is provided displayingexemplary data from a food retailer, as provided by the new site datacomponent. The new site data component 224 is an import sheet thatimports actual retailer data by month, date and hour. This data includesambient temperature and power usage per rack type.

Again referencing FIG. 10, the third component of the input blockincludes a database 226 of information regarding actual operationalparameters for specific equipment types and manufacturers. Thisinformation would be provided by CPC, Inc. of Atlanta, Ga., U.S.A. It isanticipated that this information be employed to evaluate a particularcomponent's performance to other component's in the industry as a whole.

The core calculator 210 calculates the projected energy use per racktype. The calculations are provided per ambient temperature and arecalculated using information from the input block 212 and the designblock 216 as described in more detail below. With particular referenceto FIG. 17, a screen-shot is provided displaying a portion of the corecalculator 210. As shown, a range of ambient temperatures is provided inthe left-most column. It is important to note that these temperaturesare not bin temperatures, as described above, but are provided as actualambient temperatures. The core calculator 210 calculates the totalannual energy consumption for both the compressor and condenser of aparticular type of rack. These values are shown in the right-mostcolumns of FIG. 17. For example, given an ambient temperature of zero(0) degrees Fahrenheit, the total theoretical compressor energy usage is29.34 kWh, as based upon individual suction temperatures, and the totaltheoretical condenser energy usage is 0.5 kWh.

The efficiency block output includes two main tools: a power monitoringtool 230 and an alarming tool 232, shown schematically in FIGS. 18 and19, respectively. The power monitoring tool 230 provides an evaluationof the equipment power usage as compared between a calculated value,from the core calculator 210, and the actual power usage, imported fromactual site data. The power monitoring tool 230 receives inputs from thecore calculator 210, actual site data 220, new site data 224 and itsoutput is a function of operator selectable date, time and location.With reference to FIG. 20, a screen-shot is provided for the powermonitoring tool 230. The input received from the core calculator 210includes a value for the projected use, as referenced by ambienttemperature. The actual site data 226 provides the power monitoring tool230 with the ambient temperature for each hour of the particular day.The new site data 224 provides actual use information, which ismanipulated by the power monitoring 230 tool to be summarized by hour,day and month. Using this information, the power monitoring tool 230provides a summary per rack type, whereby the actual usage is comparedto the projected usage and a difference is given. In this manner, theperformance of the refrigeration system 100 of a particular remotelocation 14 may be evaluated for efficiency.

The alarming tool 232 is shown schematically in FIG. 19 and includesalarm limits for alerting a remote location 14 when equipmentefficiencies fall below a particular limit. The alarming tool 232 may beimplemented on-site, thereby readily providing an efficiency alert toinitiate a quick correction action, as well as being implemented at themanagement center 12.

With further reference to FIG. 10, the design block output providesenergy usage calculations based upon specific design scenarios andincludes two components: a design set-up component 234 and a designresults component 236. The design set-up component 234 interacts withthe core calculator 210, providing the core calculator 210 with inputinformation and receiving calculations therefrom. With reference toFIGS. 21 and 22, a screen-shot and a schematic view are respectivelyprovided for the design set-up component 234. A user may input variousdesign scenario information and is provided with a theoretical annualenergy usage calculation.

The design set-up component 234 enables a user to input specificcomponent and operation environment variables to evaluate any one of anumber of possible operational scenarios. Each of these scenarios may besaved, deleted and retrieved, as a user desires. The user must inputspecification information for components such as a compressor,evaporator, sub-cooler, condenser and the like. With respect to thecompressor and evaporator, inputs such as refrigerant type, superheattemperature and condenser cut-out pressure are required. The sub-coolerinputs include whether a sub-cooler is present, the dropleg cut-outtemperature and fluid out temperature. The condenser inputs include thecondenser capacity (BTU/hr−F), fan power (hp), actual fanpower (%),temperature difference type, whether fan cycling or variable speed,condenser temperature difference, ambient sub-cooling and HP capacity.The design set-up component 232 uses the horsepower capacity todetermine a % horsepower.

Suction information is also provided per rack type. This informationincludes cut-in pressure, cut-out pressure and efficiency. Further, thestore specification component 222 provides the design set-up component232 with the total load (BTU/hr) for each rack type of the specificlocation.

The design set-up component 232 provides a summary table, brieflysummarizing the energy usage per rack type. The design set-up component232 further calculates a minimum condenser temperature, and suctioncalculations including cut-in temperature, cut-out temperature andaverage suction temperature.

The design results component 234 provides a more detailed breakdown ofthe power usage. With reference to FIGS. 23 and 24, a screen-shot and aschematic view are respectively provided for the design resultscomponent 234. The design results component 234 provides outputinformation as a function of whether temperature is measured by dry orwet bulb for the given remote location 14. The output informationincludes projected use in kWh for both the compressor and condenser.This information is further compiled into total use, by month, anddisplayed graphically.

Because many of the calculations are based upon the provided ASHRAEdata, it is important to consider the actual temperatures experienced ata particular location versus the average temperature provided by theASHRAE data. With reference to FIG. 25, a screen-shot is provideddisplaying a comparison between the actual average temperatures for aparticular month versus typical (i.e., ASHRAE) average temperatures forthe particular month. Considering this information, deviations betweenthe projected energy usage and actual energy usage may be morethoroughly evaluated, thereby providing a better analysis of theoperation of the refrigeration system 100.

With reference to FIG. 26, energy usage characteristics are summarizedin tabular form. The total actual and projected energy usage for allrack types is provided on a daily basis for a particular month. Othertables breakdown the total by rack type. In this manner, energy usageperformance may be quickly and easily summarized and evaluated fordetermining future operational activity.

As discussed above, the system 10 of the present invention providescontrol and evaluation algorithms, in the form of software modules 22,for predicting maintenance requirements for the various components inthe remote location 14. In the preferred embodiment, describedhereinbelow, predictive maintenance algorithms will be described withrespect to the refrigeration system 100.

A first control algorithm is provided for controlling the temperaturedifference between the refrigerant of the condenser 126 and the ambientair surrounding the condenser 126. The ambient air sensor 128 and thepressure sensor 130 of the condenser 126 are implemented to provide theinputs for the temperature difference control strategy. The pressuresensor 130 measures the refrigerant pressure exiting the condenser 126and determines a saturation temperature (T_(SAT)) from a look-up table,as a function of the type of refrigerant used. The ambient air sensor128 measures the temperature of the ambient air (T_(AMB)). Thetemperature differential (TD) is then calculated as the differencebetween the two, according to the following equation:

TD=T _(SAT) −T _(AMB)

The temperature difference algorithm further implements the followingconfiguration parameters: condenser type (i.e., differential), controltype (i.e., pressure), refrigerant type (e.g., R22, R404 a), fastrecovery, temperature difference set point and minimum temperature setpoint. In the exemplary embodiment, the temperature difference set pointis ten (10) degrees Fahrenheit and the minimum temperature set point(T_(MIN)) is seventy (70) degrees Fahrenheit. The minimum temperatureset point is the T_(SAT) corresponding to the lowest allowable condenserpressure.

A first maintenance algorithm is provided for determining whether thecondenser 126 is dirty, as shown in FIGS. 27A and 27B. Predicting thestatus of the condenser 126 is achieved by measuring the temperaturedifference for the condenser 126 over a specified period of time. Toachieve this, a fan (not shown) associated with the condenser 126 isturned on for a specified period of time (e.g., half hour) and thetemperature difference (TD) is calculated, as described above,approximately every five (5) seconds. The average of the TD calculationsis determined and stored into memory. An increase in the average TDindicates that the condenser 126 is dirty and requires cleaning. In thiscase an alarm is signaled. It should be noted, however, that the TDvalue is only meaningful if T_(AMB) is at least ten (10) degreesFahrenheit lower than T_(MIN). If the condenser 126 has been cleaned,the dirty condenser algorithm of the controller must be reset forrecording a new series of TDs.

The present invention further provides an alternative algorithm fordetecting a dirty condenser situation. Specifically, the heat rejection(Q) of the condenser 126 is evaluated. The heat rejection is a functionof an overall heat transfer coefficient (U), a heat transfer area (A)and a log mean temperature difference (LMTD), and is calculated by thefollowing equation:

Q=U×A×(LMTD)

The LMTD can be approximated as the TD measurements, described above. Avalue for Q can be approximated from the percentage output of thecompressors 102 operating with the condenser 126. Further, the aboveequation can be rearranged to solve for U:

U=Q/A×TD

Thus, U can be consistently monitored for the condenser 126. An increasein the calculated value of U is indicative of a dirty condensersituation.

A second maintenance algorithm is provided as a discharge temperaturemonitoring algorithm, shown in FIG. 28, usable to detect compressormalfunctioning. For a given suction pressure and refrigerant type, thereis a corresponding discharge temperature for the compressor 102. Thedischarge temperature monitoring algorithm compares actual dischargetemperature (T_(DIS) _(—) _(ACT)) to a calculated discharge temperature(T_(DIS) _(—) _(THR)). T_(DIS) _(—) _(ACT) is measured by thetemperature sensors 114 associated with the discharge of each compressor102. Measurements are taken at approximately 10 second intervals whilethe compressors 102 are running. T_(DIS) _(—) _(THR) is calculated as afunction of the refrigerant type, discharge pressure (P_(DIS)), suctionpressure (P_(SUC)) and suction temperature (T_(SUC)), each of which aremeasured by the associated sensors described hereinabove. An alarm value(A) and time delay (t) are also provided as presets and may be userselected. An alarm is signaled if the difference between the actual andcalculated discharge temperature is greater than the alarm value for atime period longer than the time delay. This is governed by thefollowing logic:

If (T _(DIS) _(—) _(ACT) −T _(DIS) _(—) _(THR))>A and time>t, then alarm

A third maintenance algorithm is provided as a compressor superheatmonitoring algorithm, shown schematically in FIGS. 29A and 29B, usableto detect liquid refrigerant flood back. The superheat is measured atboth the compressor suction manifold 106 and discharge header 108. Thebasis of the compressor superheat monitoring algorithm is that whenliquid refrigerant migrates to the compressor 102, superheat valuesdecrease dramatically. The present algorithm detects sudden decreases insuperheat values at the suction manifold 106 and discharge header 108for providing an alarm.

With particular reference to FIG. 29A, the superheat monitoring at thesuction manifold 106 will be described in detail. Initially, T_(SUC) andP_(SUC) are measured by the suction temperature and pressure sensors120,118 and it is further determined whether all of the compressors 102are on. A saturation temperature (T_(SAT)) is determined by referencinga look-up table using P_(SUC) and the refrigerant type. An alarm value(A) and time delay (t) are also provided as presets and may be userselected. An exemplary alarm value is fifteen (15) degrees Fahrenheit.The suction superheat (SH_(SUC)) is determined by the difference betweenT_(SUC) and T_(SAT). An alarm will be signaled if SH_(SUC) is greaterthan the alarm value for a time period longer than the time delay. Thisis governed by the following logic:

If SH _(SUC) >A and time>t, then alarm

With particular reference to FIG. 29B, the superheat monitoring at thedischarge header 108 will be described in detail. Initially, dischargetemperature (T_(DIS)) and discharge pressure (P_(DIS)) are measured bythe discharge temperature and pressure sensors 114,124. It is alsodetermined whether the particular compressor 102 is on. A saturationtemperature (T_(SAT)) is determined by referencing a look-up table usingP_(DIS) and the refrigerant type. An alarm value (A) and time delay (t)are also provided as presets and may be user selected. An exemplaryalarm value is fifteen (15) degrees Fahrenheit. The discharge superheat(SH_(DIS)) is determined by the difference between T_(DIS) and T_(SAT).An alarm is signaled if SH_(DIS) is greater than the alarm value for atime period longer than the time delay. This is governed by thefollowing logic:

 If SH _(SUC) >A and time>t, then alarm

A severe flood back alarm is also provided. A severe flood back occurswhen both a suction flood back state and a discharge flood back stateare determined. In the event that both the suction flood back alarm andthe discharge flood back alarm are signaled, as described above, thesevere flood back alarm is signaled.

A fourth maintenance algorithm is provided as a relay output monitoringalgorithm, shown schematically in FIG. 30, usable to initiate anelectrical contractor service call. In general, the relay outputmonitoring algorithm counts the number of on/off transition states for agiven relay. The number of counts is provided to a service block that ispreset with a service count value. If the number of counts is greaterthan the service count value then a service call is automatically placedto an electrical contractor.

More specifically, the algorithm initially sets an old relay state toOFF if a counter reset has been signaled or the algorithm is running forthe first time. Next, the algorithm retrieves a new relay state value(i.e., ON or OFF). The algorithm then compares the new relay state valueto the old relay state value. If they are unequal, the number counter isincreased by a single increment.

Other maintenance algorithms include: contactor count, compressorrun-time, oil checks, dirty air filter and light bulb change. Thecontactor count algorithm counts the number of times a compressor 102cycles (i.e., turned ON/OFF). A contactor count limit is provided,whereby once the number of cycles surpasses the count limit, a workorder is automatically issued by the system for signaling preventativemaintenance. Similarly, the compressor run-time algorithm monitors theamount of time a compressor 102 has run. A run-time limit is provided,whereby once the run-time surpasses the run-time limit, a work order isautomatically issued by the system for signaling routine maintenance.

As discussed in detail above, the system 10 of the present inventionprovides a method of monitoring and evaluating energy consumption forvarious components of the refrigeration system 100. It is furtheranticipated, however, that the present system 10 includes additionalalgorithms for optimizing energy efficiency of all energy using deviceswithin a location. To this end, power meters are provided forsignificant energy components of the location, including but not limitedto: refrigeration circuits and condensers, HVAC, lighting, etc. Withreference to FIG. 31, it is anticipated that the system 10 providesenergy saving algorithms for each of the identified areas, including:the VSD compressor, optimum humidity control, optimum head pressurecontrol, load management, defrost management, suction float and headpressure float.

The system 10 of the present invention further provides an alarmingsystem for alerting the management center 12 or intermediate processingcenter of particular situations. The graph provided in FIG. 32 outlinesten main alarming conditions and the corresponding operator action.These alarming conditions include: discharge air temperature sensorfailure, product temperature sensor failure, discharge air temperatureexceeded, discharge air degree-minute exceeded, product time-temperatureexceeded, product degree-minute exceeded, product FDA time-temperatureexceeded, spoiler count exceeded, pathogen count exceeded and producttemperature cycling. As shown schematically in FIG. 33, the first sixalarming conditions relate to equipment failure that would potentiallylead to food quality and safety problems. The last four alarmingconditions relate directly to food quality and safety.

As described in detail above, the system 10 provides a web-basedoperator interface for monitoring the conditions of a remote location14. With reference to FIG. 34, a screen-shot is provided detailing anexemplary user interface for monitoring the status of a particularfixture within a particular remote location 14. The centrally disposedgraph 300 provides real-time output of both the discharge airtemperature and the product temperature, as provided by the productsimulators, described above. Further provided are discharge airtemperature and product probe temperature thermometers 302,304 forrepresenting current temperature conditions. Disposed immediately belowthe real-time graph 300 is a notifications board 306 displaying each ofthe ten alarming conditions described above. Immediately below thenotifications board 306 is a shelf-life estimation board 308 that showsthe number of shelf-life hours remaining per the number of days aparticular product has been stored within a particular case. Theshelf-life estimation is calculated as described in detail above.

The description of the invention is merely exemplary in nature and,thus, variations that do not depart from the gist of the invention areintended to be within the scope of the invention. Such variations arenot to be regarded as a departure from the spirit and scope of theinvention.

What is claimed is:
 1. The method of managing a refrigeration system ata remote location, comprising: transmitting refrigeration systemperformance information from the refrigeration system at a remotelocation to a management center; analyzing said performance informationat said management center including calculating a food quality indexusing said performance information; and altering refrigeration systemconfiguration in response to said step of analyzing said performanceinformation.
 2. The method of claim 1, wherein said analyzing saidperformance information includes alarming as a function of a food safetyindex.
 3. A method of managing a refrigeration system of a retaillocation, comprising: configuring a processing center in communicationwith the refrigeration system and a management center; communicatingrefrigeration performance information from the refrigeration system tosaid processing center and said management center; analyzing saidperformance information including calculating a food quality index usingsaid performance information by at least one of said processing centerand said management center; and altering refrigeration systemconfiguration in response to said step of analyzing said performanceinformation.
 4. A method of managing a refrigeration system of a retaillocation, comprising: configuring a processing center in communicationwith the refrigeration system and a management center; communicatingrefrigeration performance information from the refrigeration system tosaid processing center and said management center; analyzing saidperformance information including calculating a food safety index usingsaid performance information by at least one of said processing centerand said management center; and altering refrigeration systemconfiguration in response to said step of analyzing said performanceinformation.
 5. The method of claim 4, further comprising alarming as afunction of said food safety index.
 6. A method of managing arefrigeration system at a retail location comprising: providing at leastone server computer in communication with the refrigeration system at aretail location through a communication network; communicatingrefrigeration system performance information from the refrigerationsystem at a retail location to said at least one server computer viasaid communication network; analyzing said performance informationincluding calculating a food quality index using said performanceinformation at said at least one server computer; and advisingmanipulation of operation of the refrigeration system based on saidanalysis of said performance information.
 7. A method of managing arefrigeration system at a retail location comprising: providing at leastone server computer in communication with the refrigeration system at aretail location through a communication network; communicatingrefrigeration system performance information from the refrigerationsystem at a retail location to said at least one server computer viasaid communication network; analyzing said performance informationincluding calculating a food safety index using said performanceinformation at said at least one server computer; and advisingmanipulation of operation of the refrigeration system based on saidanalysis of said performance information.
 8. The method of claim 7,further comprising alarming as a function of said food safety index. 9.A method of managing a refrigeration system at a retail locationcomprising: communicating refrigeration system performance informationfrom a refrigeration system to at least one server computer via acommunication network; analyzing said performance information includingcalculating a food safety index using said performance information atsaid at least one server computer; evaluating energy performance of therefrigeration system as a function of said performance information; andalarming as a function of said food safety index.